A101 - Creativity and Innovation in times of AI

Duration  4 days
Price  £2,000
Discount  10% for 2 or more attendees, 15% for 3 or more attendees from the same organisation.
Format  Live, hands-on exercises
Dates and location  Please contact us

Overview
This class provides a foundational understanding of Artificial Intelligence, statistical analysis and machine learning, the course also teaches approaches and techniques to creativity and innovation.
After the class participants will be aware not only of the AI possibilities but most importantly of their individual and collective creative potential.

Objectives
  o Develop a foundational understanding of AI, ML, and statistical analysis.
  o Be able to define and approach for a project that uses AI and machine learning.
  o Discover and apply creative thinking strategies to AI.
  o Unleash your individual and collective creativity.
  o Understand how to evaluate creativity and innovation.

Audience
Leaders, directors, managers willing to unleash the potential of AI.
Solution architects, data architects or anyone willing to have a foundational understanding and potential of AI, statistical analysis, machine learning, creativity, and innovation.

Prerequisites
No technical skills are required for this class nor any previous knowledge of AI, statistical analysis, or machine learning.

Day 1: AI
AI Introduction
  o What is AI
  o AI Examples
  o Applications and limitations

Computer Vision
  o Image analysis
  o Tags from images
  o Object detection
  o OCR
  o Face recognition

Speech and Text
  o Text to Speech
  o Speech to Text
  o Translation
  o Speaker recognition

Language
  o Named entity recognition.
  o Summarization
  o Sentiment analysis
  o Bots

Audio and Video
  o Insights, timeline, closed captions
  o Customization
  o Searching

Day 2: Statistical Analysis
What is statistical analysis.
  o Statistical analysis types
  o Descriptive statistics
  o Inferential statistics
  o Predictive statistics
  o Prescriptive statistics
  o Exploratory statistics

Survey Design
  o Types of variables
  o Sample size determination
  o Hypothesis Testing

Statistical Methods
  o Measures of central tendency
  o Hypothesis Testing
  o Correlations
  o T-tests
  o ANOVAS
  o Regressions

Experiments and conclusions
  o Conducting experiments
  o Drawing conclusions

Day 3: Machine Learning
Introduction to Machine Learning
  o Machine Learning Life cycle
  o Problem
  o Data
  o Preparation
  o Training
  o Integration
  o MLFlow

Machine learning types or approaches
  o Supervised learning
  o Unsupervised learning
  o Semi-supervised learning
  o Dimensionality reduction
  o Anomaly detection

Machine Learning Models
  o Classification
  o Prediction
  o Forecasting
  o Custom image classification and object detection
  o Natural language processing

Experiments
  o Configure
  o Run
  o Automate
  o Validate

Integration
  o Deploy
  o Test
  o Monitor  

Day 4: Creativity and Innovation
Introduction to Creativity
  o What is creativity?
  o Who can be creative?
  o Creativity life cycle
  o Technology acceptance and adoption models
  o Innovation, adaptability, creativity

Creativity approaches
  o Creativity domains
  o Types of creativity
  o Creative process
  o Factors that lead to creativity

Creativity Techniques
  o Approaches and components of creativity
  o Brainstorming
  o Mind-mapping
  o Analogies
  o Instances
  o Other

Creativity at work
  o Leading creative people
  o Creative leadership
  o Creative organizational culture and climate
  o Collective creativity

Measuring creativity
  o Criteria used to evaluate creativity.

Putting it all together
  o Responsible AI
  o Organization maturity
  o Proof of Concept
  o Quick wins and tips